import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
from tqdm import tqdm
tqdm.pandas()

def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

d0 = pd.read_csv('India_riskliteracy_dataset_above median_2024_new.csv').rename({"#":"ResponseId"}, axis = 1)
d0 = d0.loc[ d0["Please state your current occupation."] != "Student"]
d0

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d0["age_rec"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", 3,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", 3,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", 4,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", 4, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )

d0["age"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , (18 + 25) / 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", (26 + 35) / 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", (36 + 45) / 2,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", (46 + 55) / 2,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", (56 + 65) / 2,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", (66 + 75) / 2, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d0["isced"] = np.where(d0["What is currently your highest Education?"] == "Post Graduate", 1,
                        np.where(d0["What is currently your highest Education?"] == "Under Graduate", 2,
                                 np.where(d0["What is currently your highest Education?"] == "Ph.D. or higher", 1,
                                          np.where(d0["What is currently your highest Education?"] == "Diploma or vocation training", 2,
                                                   np.where(d0["What is currently your highest Education?"] == "School degree (X or XII)", 3, 0
                                                           )
                                                  )
                                         )
                                )
                       )

d0["income"] = np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "&lt; 5,00,000", 1,
                         np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "5,00,000 - 15,00,000", 2,
                                  np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "15,00,001 - 25,00,000", 3,
                                           np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "25,00,001 - 35,00,000", 4,
                                                    np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "35,00,001 - 45,00,000", 5,
                                                             np.where( d0["Please indicate your approximate annual personal income from all sources for last year"] == "&gt; 45,00,000", 6,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d0["wealth"] = d0["Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have."]

d0["isced"]

0      2
1      2
3      2
4      1
5      1
      ..
638    1
639    1
640    1
641    2
642    1
Name: isced, Length: 636, dtype: int32

d0["Certainty_3"] = d0["certainty1"] + d0["certainty2"] + d0["certainty3"]
d0["RiskComprehension_3"] = d0["riskcalculation1"] + d0["riskcalculation2"] + d0["riskcalculation3"] + d0["riskcalculation4"]
d0["GraphLiteracy_3"] = d0["graph1"] + d0["graph2"] + d0["graph3"]
d0["Numeracy_3"] = d0["numeracy1"] + d0["numeracy2"] + d0["numeracy3"]
d0["Bayesianreasoning_1"] = d0["numeracy4"]
d0["TotalScore_13"] = d0["Certainty_3"] + d0["RiskComprehension_3"] + d0["GraphLiteracy_3"] + d0["Numeracy_3"] + d0["Bayesianreasoning_1"]

d0["Certainty_%"] = d0["Certainty_3"] / 3 * 100
d0["RiskComprehension_%"] = d0["RiskComprehension_3"] / 4 * 100
d0["GraphLiteracy_%"] = d0["GraphLiteracy_3"] / 3 * 100
d0["Numeracy_%"] = d0["Numeracy_3"] / 3 * 100
d0["Bayesianreasoning_%"] = d0["Bayesianreasoning_1"] / 1 * 100

d0["TotalScore_%"] = d0["TotalScore_13"] / 14 * 100


colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_3", "RiskComprehension_3", "GraphLiteracy_3",
          "Numeracy_3", "Bayesianreasoning_1", "Certainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
          "TotalScore_13", "TotalScore_%",]

d01 = d0[colReq].copy()
d01

d1 = pd.read_csv('Indian Risk Survey_Besample_Filtered_12_2024.csv')
d11 = d1.iloc[:, 19:62].copy()
d11 = d11.loc[ d11["Q4"] != "Student"]
d11.insert(0, "ResponseId",0)
d11["ResponseId"] = d1["ResponseId"].copy()
d11

d11["Q4"].value_counts()

Q4
Salaried/Employee/Consultant in a sector other than Finance    167
Not employed                                                    50
Entrepreneur/Business Owner in a sector other than Finance      30
Stock analyst                                                   12
Insurance agent, etc                                             7
Retired                                                          3
Venture capital/private equity                                   1
Name: count, dtype: int64

d11["age"] = d11["Q2"]

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d11["age_rec"] = np.where( (d11["age"] >= 18) & (d11["age"] <= 35), 2,
                          np.where( (d11["age"] >= 36) & (d11["age"] <= 55), 3,
                                   np.where( (d11["age"] >= 56), 4, 0
                                           )
                                  )
                         )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d11["isced"] = np.where(d11["Q3"] == "Post-Graduate Program", 1,
                        np.where(d11["Q3"] == "Undergraduate Program", 2,
                                 np.where(d11["Q3"] == "Ph.D. and higher", 1,
                                          np.where(d11["Q3"] == "Diploma and Vocational Training", 2,
                                                   np.where(d11["Q3"] == "Secondary School (11th to 12th Std.)", 3,
                                                            np.where( d11["Q3"] == "Primary School (up to 10th Std.)", 3,
                                                                     np.where( d11["Q3"] == "M.Phil.", 1, 0
                                                                             )
                                                                    )
                                                           )
                                                  )
                                         )
                                )
                       )

d11["income"] = np.where(d11["Q15a"] == "< INR 500,000", 1,
                         np.where(d11["Q15a"] == "INR 500,001 – INR 15,00,000", 2,
                                  np.where(d11["Q15a"] == "INR 1500,001 – INR 30,00,000", 3,
                                           np.where(d11["Q15a"] == "INR 30,00,001 – INR 50,00,000", 5,
                                                    np.where(d11["Q15a"] == "INR 50,00,001 – INR 75,00,000", 6,
                                                             np.where( d11["Q15a"] == "> INR 75,00,000", 6,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d11["wealth"] = d11["Q14b.1"]

matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d11[ matchCol ] = 0

d11.columns

Index(['ResponseId', 'Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5_1', 'Q6_1', 'Q8_1',
       'Q8_2', 'Q8_3', 'Q8_4', 'Q8_5', 'Q8_6', 'Q8_7', 'Q8_8', 'Q9b_1',
       'Q9b_4', 'Q9b_5', 'Q9b_6', 'Q9b_7', 'Q9b_8', 'Q9b_9', 'Q11a_1',
       'Q11a_2', 'Q11a_3', 'Q11b', 'Q11c', 'Q11d', 'Q11h', 'Attention Check',
       'Q11i', 'Q12a', 'Q12b', 'Q12c', 'Q13a', 'Q13b', 'Q13c', 'Q13d', 'Q14a',
       'Q14b', 'Q14c', 'Q15a', 'Q14b.1', 'age', 'age_rec', 'isced', 'income',
       'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5', 'q8_3',
       'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6', 'q8_7',
       'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1', 'q10_4'],
      dtype='object')

d11["q8_2_1"] = np.where(d11["Q11b"].str.contains("HIV test"), 1, 2)
d11["q8_2_2"] = np.where(d11["Q11b"].str.contains("Fingerprint"), 1, 2)
d11["q8_2_3"] = np.where(d11["Q11b"].str.contains("DNA test"), 1, 2)
d11["q8_2_4"] = np.where(d11["Q11b"].str.contains("Cancer screening test"), 1, 2)
d11["q8_2_5"] = np.where(d11["Q11b"].str.contains("Professional horoscope"), 1, 2)

d11["q8_3"] = np.where(d11["Q11c"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d11["Q11c"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d11["q8_4"] = np.where(d11["Q11d"] == "The growth rate will be 0.4% on average each year", 1,
                          np.where(d11["Q11d"] == "The growth rate over five years will be exactly 2%", 2,
                                  np.where(d11["Q11d"] == "The growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d11["Q11d"] == "It is not possible to predict the growth rate with certainty", 4,0
                                                  )
                                          )
                                  )
                         )

d11["q8_5berlin_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5london_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5paris_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)

d11["q8_6"] = np.where(d11["Q11i"] == "The medication increases recovery by 100%", 1,
                          np.where(d11["Q11i"] == "The medication increases recovery by 50%", 2,
                                   np.where(d11["Q11i"] == "The medication increases recovery by 2%", 3,
                                            np.where(d11["Q11i"] == "None of the above is implied", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q8_7"] = np.where(d11["Q11h"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d11["Q11h"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d11["Q11h"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d11["Q11h"] == "It is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d11["q9_1_1"] = d11["Q14a"].copy()
d11["q9_2_1"] = d11["Q14b"].copy()

d11["q9_3"] = np.where(d11["Q14c"] == "Crosicol", 1,
                          np.where(d11["Q14c"] == "Hertinol", 2,
                                   np.where(d11["Q14c"] == "They are equal", 3,
                                            np.where(d11["Q14c"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q10_1_1"] = d11["Q13a"].copy()
d11["q10_2_1"] = d11["Q13b"].copy()
d11["q10_3_1"] = d11["Q13c"].copy()

d11["q10_4"] = np.where(d11["Q13d"] == "9 out of 59", 1,
                          np.where(d11["Q13d"] == "9 out of 10", 2,
                                   np.where(d11["Q13d"] == "59 out of 1000", 3,
                                            np.where(d11["Q13d"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["ResponseId"].nunique()

270

scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcalculation1", "riskcalculation2", "riskcalculation3", "riskcalculation4"]
d11[scoreColumns] = 0

# Assigning scores

def scoring1(d11Facet):
    d11Facet.loc[ d11Facet["q8_2_1"] == 2, "certainty1"] = 1
    d11Facet.loc[ d11Facet["q8_2_2"] == 2, "certainty2"] = 1
    d11Facet.loc[ d11Facet["q8_2_3"] == 2, "certainty3"] = 1
    d11Facet.loc[ d11Facet["q8_2_4"] == 2, "certainty4"] = 1
    d11Facet.loc[ d11Facet["q8_2_5"] == 2, "certainty5"] = 1

    d11Facet.loc[ d11Facet["q8_3"] == 1, "uncertainty1"] = 1
    d11Facet.loc[ d11Facet["q8_4"] == 4, "uncertainty2"] = 1

    d11Facet.loc[ d11Facet["q8_5berlin_1"] == 2, "numeracy1"] = 1
    d11Facet.loc[ d11Facet["q8_5london_1"] == 1, "numeracy2"] = 1
    d11Facet.loc[ d11Facet["q8_5paris_1"] == 1, "numeracy3"] = 1
    d11Facet.loc[ d11Facet["q8_6"] == 1, "numeracy4"] = 1
    d11Facet.loc[ d11Facet["q8_7"] == 4, "numeracy5"] = 1

    d11Facet.loc[ d11Facet["q9_1_1"] == 25, "graph1"] = 1
    d11Facet.loc[ d11Facet["q9_2_1"] == 20, "graph2"] = 1
    d11Facet.loc[ d11Facet["q9_3"] == 3, "graph3"] = 1

    d11Facet.loc[ d11Facet["q10_1_1"] == 500, "riskcalculation1"] = 1
    d11Facet.loc[ d11Facet["q10_2_1"] == 10, "riskcalculation2"] = 1
    d11Facet.loc[ (d11Facet["q10_3_1"] == 0.1) | (d11Facet["q10_3_1"] == ".1") | (d11Facet["q10_3_1"] == ",1") , "riskcalculation3"] = 1
    d11Facet.loc[ d11Facet["q10_4"] == 1, "riskcalculation4"] = 1

    # Assigning total scores
    #d11Facet["Certainty score_5"] = d11Facet["certainty1"] + d11Facet["certainty2"] + d11Facet["certainty3"] + d11Facet["certainty4"] + d11Facet["certainty5"]
    #d11Facet["Uncertainty score_5"] = d11Facet["uncertainty1"] + d11Facet["uncertainty2"]

    #d11Facet["Number Comprehension score_5"] = d11Facet["numeracy1"] + d11Facet["numeracy2"] + d11Facet["numeracy3"] + d11Facet["numeracy4"] + d11Facet["numeracy5"]
    #d11Facet["Graph Comprehension score_5"] = d11Facet["graph1"] + d11Facet["graph2"] + d11Facet["graph3"]

    #d11Facet["Calculation score_4"] = d11Facet["riskcalculation1"] + d11Facet["riskcalculation2"] + d11Facet["riskcalculation3"] + d11Facet["riskcalculation4"]

    #d11Facet["Total Score_19"] = d11Facet["Certainty score_5"] + d11Facet["Uncertainty score_5"] + d11Facet["Number Comprehension score_5"] + d11Facet["Graph Comprehension score_5"] + d11Facet["Calculation score_4"]

    return d11Facet

d11Facet = d11.groupby(["ResponseId"]).progress_apply(scoring1)

100%|███████████████████████████████████████████████████████████████████████████████| 270/270 [00:01<00:00, 165.39it/s]

d11Facet

d11 = d11Facet.copy().reset_index(drop = True)

d11["Certainty_3"] = d11["certainty1"] + d11["certainty2"] + d11["certainty3"]
d11["RiskComprehension_3"] = d11["riskcalculation1"] + d11["riskcalculation2"] + d11["riskcalculation4"]
d11["GraphLiteracy_3"] = d11["graph1"] + d11["graph2"] + d11["graph3"]
d11["Numeracy_3"] = d11["numeracy1"] + d11["numeracy2"] + d11["numeracy3"]
d11["Bayesianreasoning_1"] = d11["numeracy4"]
d11["TotalScore_13"] = d11["Certainty_3"] + d11["RiskComprehension_3"] + d11["GraphLiteracy_3"] + d11["Numeracy_3"] + d11["Bayesianreasoning_1"]

d11["Certainty_%"] = d11["Certainty_3"] / 3 * 100
d11["RiskComprehension_%"] = d11["RiskComprehension_3"] / 4 * 100
d11["GraphLiteracy_%"] = d11["GraphLiteracy_3"] / 3 * 100
d11["Numeracy_%"] = d11["Numeracy_3"] / 3 * 100
d11["Bayesianreasoning_%"] = d11["Bayesianreasoning_1"] / 1 * 100

d11["TotalScore_%"] = d11["TotalScore_13"] / 13 * 100


colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_3", "RiskComprehension_3", "GraphLiteracy_3",
          "Numeracy_3", "Bayesianreasoning_1", "Certainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
          "TotalScore_13", "TotalScore_%",]

d12 = d11[colReq].copy()
d12

df2 = pd.concat([d01, d12], axis = 0).reset_index(drop = True)

df2

# The following graph represents frequency of each data point on "TotalScore_13" or the total score out of 19 questions across the sample.

df2['TotalScore_13'].plot(kind = 'hist', xticks = np.arange(0, 15, step=1), xlabel = 'TotalScore_13', title = 'Frequency of Scores' )

<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_13', ylabel='Frequency'>

# Absolute mean scores for each facet

(df2[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')

<Axes: title={'center': 'Absolute mean of scores for above data set'}>

# Normalised mean scores for each facet

df2[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = 'Normalised mean of scores for above data set')

<Axes: title={'center': 'Normalised mean of scores for above data set'}>

# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3

# Absolute mean score for each facet (Sort by Income)

df3[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Income)')

<Axes: title={'center': 'Absolute mean score for each facet (Sort by Income)'}>

# Normalised mean score for each facet (Sort by Income)

(df3[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Income)')

<Axes: title={'center': 'Normalised mean score for each facet (Sort by Income)'}>

# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()

# Count of responses for each category (sorted by income)

df3.groupby('income')[['ResponseId']].count()

# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee11ae790>

# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_3','RiskComprehension_3','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee1752f10>

# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee16c9050>

# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee117dd50>

# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee0b0db10>

# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_13')

<Axes: xlabel='income', ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax, label='Certainty_3')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_3')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax, label='Numeracy_3')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_13 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_13')

<Axes: xlabel='income', ylabel='TotalScore_13'>

# Violine Plot for Certainty_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_3')

<Axes: xlabel='income', ylabel='Certainty_3'>

# Violine Plot for RiskComprehension_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_3')

<Axes: xlabel='income', ylabel='RiskComprehension_3'>

# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')

<Axes: xlabel='income', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_3')

<Axes: xlabel='income', ylabel='Numeracy_3'>

# Violine Plot for TotalScore_13 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')

<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>

# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4

# Absolute mean score for each facet (Sort by Wealth)

df4[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1','TotalScore_13']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Wealth)')

<Axes: title={'center': 'Absolute mean score for each facet (Sort by Wealth)'}>

# Normalised mean score for each facet (Sort by Wealth)

(df4[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Wealth)')

<Axes: title={'center': 'Normalised mean score for each facet (Sort by Wealth)'}>

# Line Plot of Absolute Total Facet score vs sorted wealth

df4.plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total responses line plot sorted by Wealth',xlabel = 'Unique responses sorted by wealth', ylabel = 'TotalScore_13')

<Axes: title={'center': 'Total responses line plot sorted by Wealth'}, xlabel='Unique responses sorted by wealth', ylabel='TotalScore_13'>

# Absolute Facet scores line plot from low to high wealth 
# I do understand that this is non readable

df4[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].plot( kind = 'line', title = 'Absolute Facet scores line plot', use_index = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee1374a90>

# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_13')

<Axes: ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax1, label='Certainty_3')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_3')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax1, label='Numeracy_3')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()

df4.loc[df4['Quartile Number']==1]

# This graph is basically a line plot of the TotalScore_13 across first quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==1].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_13')

<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_13'>

# Trend line for Absolute Total Facet Score vs unique Q14. Income reponses in Quartile 1

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'TotalScore_13')

<Axes: ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 1

fig1, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df4.loc[df4['Quartile Number']==2]

# This graph is basically a line plot of the TotalScore_13 across second quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==2].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_13')

<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_13'>

# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 2

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'TotalScore_13')

<Axes: ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 2

fig1, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax3, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax3, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax3, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax3, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax3, label='Bayesianreasoning_1' )

ax3.set(ylabel='Scores', xlabel='wealth')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df4.loc[df4['Quartile Number']==3]

# This graph is basically a line plot of the TotalScore_13 across third quartile unique responses which are ordered by wealth

df4.loc[df4['Quartile Number']==3].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q3',xlabel = 'Unique responses', ylabel = 'TotalScore_13')

<Axes: title={'center': 'Total Score line plot sorted by Wealth Q3'}, xlabel='Unique responses', ylabel='TotalScore_13'>

# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 3

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'TotalScore_13')

<Axes: ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 3

fig1, ax4 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax4, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax4, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax4, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax4, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax4, label='Bayesianreasoning_1' )

ax4.set(ylabel='Scores', xlabel='wealth')
ax4.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df4.loc[df4['Quartile Number']==4]

# This graph is basically a line plot of the TotalScore_13 across fourth quartile unique responses which are ordered by Wealth

df4.loc[df4['Quartile Number']==4].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_13'], title = 'Total Score line plot sorted by Wealth Q4',xlabel = 'Unique responses', ylabel = 'TotalScore_13')

<Axes: title={'center': 'Total Score line plot sorted by Wealth Q4'}, xlabel='Unique responses', ylabel='TotalScore_13'>

# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 4

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'TotalScore_13')

<Axes: ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 4

fig1, ax5 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax5, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax5, label='RiskComprehension_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax5, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax5, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax5, label='Bayesianreasoning_1' )

ax5.set(ylabel='Scores', xlabel='wealth')
ax5.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee0df5fd0>

# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee0dafc90>

# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee76242d0>

# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee76afad0>

# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee2d35950>

# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_13')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_13 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_13')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_13'>

# Violine Plot for Certainty_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_3')

<Axes: xlabel='Quartile Number', ylabel='Certainty_3'>

# Violine Plot for RiskComprehension_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_3')

<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_3'>

# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')

<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_3')

<Axes: xlabel='Quartile Number', ylabel='Numeracy_3'>

# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')

<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>

# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5

# since we didnt drop any values, the dataset is essentially the same as df2, but sorted

# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()

# Count of responses for each category isced

df5.groupby('isced')[['ResponseId']].count()

# Absolute Total Facet scores mean line plot from low to high education

df5.groupby('isced')[['TotalScore_13']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee82ad710>

# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_3','RiskComprehension_3','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee83a4650>

# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee8455b90>

# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee836f990>

# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27ee81fe7d0>

# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_13')

<Axes: xlabel='isced', ylabel='TotalScore_13'>

# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax6, label='Certainty_3')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_3')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax6, label='Numeracy_3')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_13 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_13')

<Axes: xlabel='isced', ylabel='TotalScore_13'>

# Violine Plot for Certainty_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_3')

<Axes: xlabel='isced', ylabel='Certainty_3'>

# Violine Plot for RiskComprehension_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_3')

<Axes: xlabel='isced', ylabel='RiskComprehension_3'>

# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')

<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_3')

<Axes: xlabel='isced', ylabel='Numeracy_3'>

# Violine Plot for TotalScore_13 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')

<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>

# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()

# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()

# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_3', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()

df7.loc[df7['isced']==2].describe()

df7.loc[df7['isced']==3].describe()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_13'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_13', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_3', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_3', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df7

# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

df7.loc[(df7['age_rec']==2)].describe()

df7.loc[(df7['age_rec']==3)].describe()

df7.loc[(df7['age_rec']==4)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_13'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

# Age median method
# Age_median

age_m = df2["age"].median()
age_m

30.5

df7["income"].value_counts()

income
2    371
1    250
3    148
5     47
4     46
6     44
Name: count, dtype: int64

df7.loc[(df7['age'] < age_m)].describe()

df7.loc[(df7['age'] >= age_m)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] >= age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['TotalScore_13'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_13'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['TotalScore_13'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['TotalScore_13'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Certainty_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Certainty_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_3', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['RiskComprehension_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['RiskComprehension_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['RiskComprehension_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Numeracy_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Numeracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

	ResponseId	Unnamed: 1	#.1	Please indicate your Gender.	Please mark your age (in years)	What is currently your highest Education?	Please state your current occupation.	How do you describe your willingness to take financial risk in general?	Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?	If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.	I only buy a financial product I understand.	I trust doctors.	When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.	I always keep in mind that everything I do on the web could be used to my disadvantage.	In my household, we/I spend:	In my household, we/I	_Distribution 1_\n\nHow risky do you perceive the investment to be?	_Distribution 2_\n\nHow risky do you perceive the investment to be?	_Distribution 3_\n\nHow risky do you perceive the investment to be?	_Distribution 4_\n\nHow risky do you perceive the investment to be?	_Distribution 5_\n\nHow risky do you perceive the investment to be?	Distribution 6\n\nHow risky do you perceive the investment to be?	Distribution 7\n\nHow risky do you perceive the investment to be?	Distribution 8\n\nHow risky do you perceive the investment to be?	Mumbai A = 9 out of 10000 OR B = 1 out of 1000	Bengaluru A = 0.7% OR B = 0.099%	Kolkata A = 0.61% OR B = 6 out of 10000	HIV test	Fingerprint	DNA test	Cancer screening test	Professional horoscope	A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true?	There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that…	Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?	It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?	The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?	A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:	Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies:	Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.	In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.	In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets	About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]	Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?	The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?	In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?	Please indicate your approximate annual personal income from all sources for last year	Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.	uid	Response Type	Start Date (UTC)	Stage Date (UTC)	Submit Date (UTC)	Network ID	LTA	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcalculation1	riskcalculation2	riskcalculation3	riskcalculation4
0	00dn8csjzlekx6gzhrcv00dn8csj32tz	547	00dn8csjzlekx6gzhrcv00dn8csj32tz	Male	25 - 35	Under Graduate	Investment Professional, i.e. stock broker/tra...	7	10	Completely	Just about	Completely	Completely	Just about	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	7	7	7	6	6	6	6	7	A	B	A	Yes	Yes	Yes	Yes	Yes	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 50%	30% of the banks customers will default next year	30%	it is not possible to determine which of the a...	The medication increases recovery by 50%	30	20	30	9 out of 10	40	60	They are equal	5,00,000 - 15,00,000	50000	MX9oX1724844773oYI	completed	2024-08-28 11:35:35	NaN	2024-08-28 12:02:05	f30fdd629e	18.402778	1	1	1	1	1	1	1	1	2	1	2	4	40	60	3	30	20	30	2	0	0	0	0	0	1	0	0	0	1	0	1	0	0	1	0	0	0	0
1	00ujdxbfoya0donu8r00ujcjdkojc99x	428	00ujdxbfoya0donu8r00ujcjdkojc99x	Female	25 - 35	Under Graduate	Employee/Consultant in Other than the Finance ...	7	8	Completely	Completely	Completely	Moderately	Moderately	less than half of the household income and sav...	write down budget and spending, and have an em...	2	1	1	1	0	2	0	0	B	B	A	Yes	Yes	Yes	Yes	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	50%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 50%	500	100	1	59 out of 1000	50	10	They are equal	< 5,00,000	1500000	MXic21725178744djB	completed	2024-09-01 08:23:04	NaN	2024-09-01 09:24:44	1c1d51349c	42.824074	1	1	1	1	2	2	3	2	2	1	2	2	50	10	3	500	100	1	3	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	1	0	0	0
3	022xoawhrghfhv4a1g022xmz7hb0m41f	442	022xoawhrghfhv4a1g022xmz7hb0m41f	Female	46 - 55	Under Graduate	Entrepreneur or Own Business	5	4	Somewhat	Somewhat	Moderately	Just about	Somewhat	all or more than the household income, even th...	spend as it feel right, and do not have an eme...	4	4	4	4	4	4	4	4	B	A	B	Yes	Yes	Yes	No	No	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 60%	30% of the banks customers will default next year	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	1	1	59 out of 1000	17	65	They are equal	< 5,00,000	15000000	MXFDH1725178659xRn	completed	2024-09-01 08:21:14	NaN	2024-09-01 08:33:49	fbd8962401	8.738426	1	1	1	2	2	1	1	2	1	2	3	1	17	65	3	500	1	1	3	0	0	0	1	1	1	0	1	1	0	0	0	0	0	1	1	0	0	0
4	02pxtdbyibecqqfvwlw02pxwfbane9zd	558	02pxtdbyibecqqfvwlw02pxwfbane9zd	Female	46 - 55	Post Graduate	Employee/Consultant in Other than the Finance ...	6	10	Not at all	Completely	Completely	Completely	Moderately	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	5	5	6	5	7	5	0	5	B	A	A	Yes	Yes	Yes	No	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	an answer is not possible based on the informa...	30% of the banks customers will default next year	30%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	1000	1	100	9 out of 10	20	20	Can’t say	5,00,000 - 15,00,000	3000000	MXIpM1724844742xet	completed	2024-08-28 11:35:05	NaN	2024-08-28 11:48:47	e8b668d11b	9.513889	1	1	1	2	1	2	4	2	1	1	4	1	20	20	4	1000	1	100	2	0	0	0	1	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0
5	037aefjdt26mnjd102nz0pk037aedfhc	544	037aefjdt26mnjd102nz0pk037aedfhc	Male	46 - 55	Post Graduate	Employee/Consultant in Other than the Finance ...	3	6	Somewhat	Somewhat	Somewhat	Somewhat	Somewhat	more than half of the household income and sav...	write down budget and spending, but have no em...	3	1	6	1	2	6	7	5	A	B	A	No	No	Yes	No	No	Irrespective of the quality of the study, futu...	it is not possible to predict the growth rate ...	an answer is not possible based on the informa...	Banks similar to Indigo will default 30% of th...	60%	70 in 100 people prior to the intervention to ...	None of the above is implied.	1000	1	10	9 out of 59	50	20	Hertinol	5,00,000 - 15,00,000	7000000	MXpv61724844846UVs	completed	2024-08-28 11:35:50	NaN	2024-08-28 11:45:54	f51b723011	6.990741	2	2	1	2	2	3	4	1	2	1	4	3	50	20	2	1000	1	10	1	1	1	0	1	1	0	1	0	0	1	0	0	0	1	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
638	zeoufbqlngfunzzeoymhtj2sp5cw6lur	396	zeoufbqlngfunzzeoymhtj2sp5cw6lur	Male	25 - 35	Post Graduate	Investment Professional, i.e. stock broker/tra...	3	8	Not at all	Somewhat	Somewhat	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	3	4	4	3	6	6	3	4	B	A	A	No	Yes	No	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be exactl...	the stock price increased by 100%	Banks similar to Indigo will default 30% of th...	50%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	500	100	1	59 out of 1000	50	40	Crosicol	< 5,00,000	50000	MXFoD1725258821KAh	completed	2024-09-02 06:36:01	NaN	2024-09-02 06:46:38	c31fa70c8d	7.372685	2	1	2	2	2	2	2	2	1	1	1	3	50	40	1	500	100	1	3	1	0	1	1	1	0	0	1	1	1	1	0	0	0	0	1	0	0	0
639	zfemo30rg0ekva18x1kjwzfemo2wpg2p	185	zfemo30rg0ekva18x1kjwzfemo2wpg2p	Male	36 - 45	Ph.D. or higher	Employee/Consultant in Other than the Finance ...	6	8	Not at all	Moderately	Just about	Not at all	Not at all	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	3	1	4	2	0	5	0	2	B	A	A	No	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate over five years will be betwee...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	30%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 100%	500	10	1	59 out of 1000	25	20	Can’t say	15,00,001 - 25,00,000	8500000	MXUxt1725260651heG	completed	2024-09-02 07:08:18	NaN	2024-09-02 07:23:09	fbb956cffc	10.312500	2	1	1	2	2	4	3	2	1	1	1	2	25	20	4	500	10	1	3	1	0	0	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
640	znqvw4t38br3072znqvwvjsgb6wvj7nt	305	znqvw4t38br3072znqvwvjsgb6wvj7nt	Female	36 - 45	Post Graduate	Entrepreneur or Own Business	5	5	Somewhat	Completely	Just about	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	0	0	0	1	0	0	0	0	B	B	B	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	it is not possible to predict the growth rate ...	the stock price increased by 50%	30% of the banks customers will default next year	80%	it is not possible to determine which of the a...	The medication increases recovery by 2%	500	10	1	9 out of 59	25	20	Hertinol	5,00,000 - 15,00,000	2000000	MXd921725259652XDJ	completed	2024-09-02 06:49:56	NaN	2024-09-02 07:17:20	1964f49e1c	19.027778	1	1	1	2	2	3	4	2	2	2	3	4	25	20	2	500	10	1	1	0	0	0	1	1	0	1	1	0	0	0	1	1	1	0	1	1	0	1
641	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	306	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	Male	18 - 25	Under Graduate	Entrepreneur or Own Business	6	9	Completely	Completely	Moderately	Completely	Moderately	all or more than our household income, because...	have an emergency fund, and spend as it feels ...	6	3	7	1	2	7	7	3	A	A	B	No	Yes	Yes	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 60%	Banks similar to Indigo will default 30% of th...	80%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	750	10	1	59 out of 1000	50	10	They are equal	5,00,000 - 15,00,000	60000000	MXfmg1725259705Fkj	completed	2024-09-02 06:49:52	NaN	2024-09-02 07:01:04	03aa77ec48	7.777778	2	1	1	2	2	2	3	1	1	2	4	1	50	10	3	750	10	1	3	1	0	0	1	1	0	0	0	1	0	0	0	0	0	1	0	1	0	0
642	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	95	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	Male	25 - 35	Post Graduate	Entrepreneur or Own Business	6	7	Somewhat	Just about	Completely	Moderately	Somewhat	more than half of the household income and sav...	spend as it feel right, and do not have an eme...	6	0	2	5	3	7	0	4	B	A	A	No	Yes	Yes	Yes	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	10	1	59 out of 1000	25	60	Can’t say	15,00,001 - 25,00,000	5000000	MXjp51725955476hMt	completed	2024-09-10 08:06:34	NaN	2024-09-10 08:16:35	f68294192e	6.956019	2	1	1	1	1	2	4	2	1	1	3	1	25	60	4	500	10	1	3	1	0	0	0	0	0	1	1	1	1	0	0	1	0	0	1	1	0	0

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
0	00dn8csjzlekx6gzhrcv00dn8csj32tz	30.5	2	2	2	50000	0	0	1	1	0	0.000000	0.0	33.333333	33.333333	0.0	2	14.285714
1	00ujdxbfoya0donu8r00ujcjdkojc99x	30.5	2	2	1	1500000	0	1	1	2	0	0.000000	25.0	33.333333	66.666667	0.0	4	28.571429
3	022xoawhrghfhv4a1g022xmz7hb0m41f	50.5	3	2	1	15000000	0	1	1	2	0	0.000000	25.0	33.333333	66.666667	0.0	4	28.571429
4	02pxtdbyibecqqfvwlw02pxwfbane9zd	50.5	3	1	2	3000000	0	0	1	3	0	0.000000	0.0	33.333333	100.000000	0.0	4	28.571429
5	037aefjdt26mnjd102nz0pk037aedfhc	50.5	3	1	2	7000000	2	1	1	1	0	66.666667	25.0	33.333333	33.333333	0.0	5	35.714286
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
638	zeoufbqlngfunzzeoymhtj2sp5cw6lur	30.5	2	1	1	50000	2	1	0	3	1	66.666667	25.0	0.000000	100.000000	100.0	7	50.000000
639	zfemo30rg0ekva18x1kjwzfemo2wpg2p	40.5	3	1	3	8500000	1	2	2	3	1	33.333333	50.0	66.666667	100.000000	100.0	9	64.285714
640	znqvw4t38br3072znqvwvjsgb6wvj7nt	40.5	3	1	2	2000000	0	3	2	1	0	0.000000	75.0	66.666667	33.333333	0.0	6	42.857143
641	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	21.5	2	2	2	60000000	1	1	1	1	0	33.333333	25.0	33.333333	33.333333	0.0	4	28.571429
642	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	30.5	2	1	3	5000000	1	2	1	3	0	33.333333	50.0	33.333333	100.000000	0.0	7	50.000000

	ResponseId	Q0	Q1	Q2	Q3	Q4	Q5_1	Q6_1	Q8_1	Q8_2	Q8_3	Q8_4	Q8_5	Q8_6	Q8_7	Q8_8	Q9b_1	Q9b_4	Q9b_5	Q9b_6	Q9b_7	Q9b_8	Q9b_9	Q11a_1	Q11a_2	Q11a_3	Q11b	Q11c	Q11d	Q11h	Attention Check	Q11i	Q12a	Q12b	Q12c	Q13a	Q13b	Q13c	Q13d	Q14a	Q14b	Q14c	Q15a	Q14b.1
1	R_4F8RqnI7xnwXmBY	Yes, I would like to participate in the study ...	Male	41	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	6	9	4	4	4	4	4	5	4	5	10.0	30.0	0	0	20.0	40	0.0	2	1	1	HIV test,Fingerprint,DNA test	Irrespective of the quality of the study, futu...	It is not possible to predict the growth rate ...	100 in 10,000 people prior to the intervention...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	0.10	59 out of 1000	25.0	20	Can’t say	INR 500,001 – INR 15,00,000	5000000.0
4	R_4TMr0yMiNpsJBr5	Yes, I would like to participate in the study ...	Male	37	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	3	1	4	5	5	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	2	70.0	10.0	0	0	0.0	10	10.0	2	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	100 in 10,000 people prior to the intervention...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	0.10	59 out of 1000	25.0	20	They are equal	< INR 500,000	1000000.0
7	R_4lDL0HKQBvz3esp	Yes, I would like to participate in the study ...	Male	29	Undergraduate Program	Entrepreneur/Business Owner in a sector other ...	7 (willing to take risk)	3	1 (strongly disagree)	2	3	4	6 (strongly agree)	4	4	1 (strongly disagree)	70.0	10.0	0	5	0.0	10	5.0	2	2	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	True	500	10	0.10	9 out of 10	25.0	20	They are equal	INR 500,001 – INR 15,00,000	500000.0
8	R_4MNcacFfRQwplfz	Yes, I would like to participate in the study ...	Male	27	Post-Graduate Program	Salaried/Employee/Consultant in a sector other...	0 (unwilling to take risk)	0 (no investment experience)	3	4	3	3	6 (strongly agree)	4	4	5	20.0	10.0	0	0	20.0	30	20.0	2	2	1	Fingerprint	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	100 in 10,000 people prior to the intervention...	Vase	None of the above is implied	More than $102	Less than today with the money in this account	False	500	100	0.10	9 out of 10	10.0	20	Crosicol	INR 500,001 – INR 15,00,000	10000000.0
9	R_4Ecp6YekNbrMCwi	Yes, I would like to participate in the study ...	Male	39	Post-Graduate Program	Entrepreneur/Business Owner in a sector other ...	4	7	5	5	6 (strongly agree)	5	5	6 (strongly agree)	5	6 (strongly agree)	80.0	20.0	0	0	0.0	0	0.0	2	1	1	Fingerprint	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	False	1000	10	10.00	9 out of 10	25.0	60	Crosicol	< INR 500,000	165000.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
364	R_4zPiSk23ayGvFD4	Yes, I would like to participate in the study ...	Male	70	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	6	4	4	4	4	5	5	5	4	15.0	20.0	10	0	25.0	30	0.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	1.00	59 out of 1000	25.0	20	Crosicol	INR 1500,001 – INR 30,00,000	7000000.0
365	R_47eRbAoGWc5Ttzb	Yes, I would like to participate in the study ...	Male	48	Post-Graduate Program	Salaried/Employee/Consultant in a sector other...	0 (unwilling to take risk)	1	4	5	4	5	5	2	3	6 (strongly agree)	50.0	10.0	10	20	2.0	5	3.0	1	1	1	Professional horoscope	Irrespective of the quality of the study, futu...	The growth rate over five years will be exactl...	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	Refuse to answer	Exactly the same as today with the money in th...	False	500	68	0.01	59 out of 100	50.0	40	They are equal	< INR 500,000	50000.0
366	R_4eWLoP4wkX5K1kl	Yes, I would like to participate in the study ...	Male	22	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	5	6	5	5	4	5	5	6 (strongly agree)	5	4	70.0	15.0	0	0	15.0	0	0.0	2	1	2	Cancer screening test	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	More than $102	More than today with the money in this account	False	500	50	0.10	9 out of 59	25.0	20	They are equal	< INR 500,000	800000.0
368	R_42y9IJJWALsuHPF	Yes, I would like to participate in the study ...	Male	28	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	6	7	6 (strongly agree)	3	5	5	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	4.0	0.0	0	0	6.0	40	50.0	2	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	600	10	0.10	9 out of 10	25.0	5	Hertinol	INR 1500,001 – INR 30,00,000	4500000.0
369	R_8k6D0jzzHCC5X3Z	Yes, I would like to participate in the study ...	Male	25	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	7 (willing to take risk)	8	6 (strongly agree)	5	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	5.0	5.0	5	5	30.0	20	30.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	500	50.00	59 out of 1000	25.0	10	Hertinol	> INR 75,00,000	8000000.0

		ResponseId	Q0	Q1	Q2	Q3	Q4	Q5_1	Q6_1	Q8_1	Q8_2	Q8_3	Q8_4	Q8_5	Q8_6	Q8_7	Q8_8	Q9b_1	Q9b_4	Q9b_5	Q9b_6	Q9b_7	Q9b_8	Q9b_9	Q11a_1	Q11a_2	Q11a_3	Q11b	Q11c	Q11d	Q11h	Attention Check	Q11i	Q12a	Q12b	Q12c	Q13a	Q13b	Q13c	Q13d	Q14a	Q14b	Q14c	Q15a	Q14b.1	age	age_rec	isced	income	wealth	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcalculation1	riskcalculation2	riskcalculation3	riskcalculation4
ResponseId
R_1YkMM2lMB9aEuVL	150	R_1YkMM2lMB9aEuVL	Yes, I would like to participate in the study ...	Female	41	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	3	3	3	4	3	3	3	3	3	4	85.0	0.0	0	0	5.0	10	0.0	1	1	1	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 50%	Less than $102	Exactly the same as today with the money in th...	True	1000	10	1.000	59 out of 1000	9.0	20	Crosicol	< INR 500,000	25000.0	41	3	2	1	25000.0	2	2	1	2	2	2	3	1	1	1	2	1	9.0	20	1	1000	10	1.000	3	1	1	0	1	1	0	0	0	1	1	0	0	0	1	0	0	1	0	0
R_401xXJ9E3ZgU9gu	96	R_401xXJ9E3ZgU9gu	Yes, I would like to participate in the study ...	Male	33	Undergraduate Program	Stock analyst	7 (willing to take risk)	8	5	4	5	5	5	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	0.0	0.0	10	0	30.0	60	0.0	2	1	1	Fingerprint,DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	100 in 10,000 people prior to the intervention...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	1	0.001	59 out of 1000	25.0	20	They are equal	INR 500,001 – INR 15,00,000	16000000.0	33	2	2	2	16000000.0	2	1	1	2	2	2	3	2	2	2	1	2	25.0	20	3	500	1	0.001	3	1	0	0	1	1	0	0	1	0	0	1	0	1	1	1	1	0	0	0
R_40VyTmJ6i96wUP4	235	R_40VyTmJ6i96wUP4	Yes, I would like to participate in the study ...	Female	34	Post-Graduate Program	Not employed	2	0 (no investment experience)	3	2	2	3	3	3	2	3	40.0	25.0	10	10	15.0	0	0.0	1	2	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	Refuse to answer	Less than today with the money in this account	Do not know	698	352	1.000	59 out of 1000	30.0	20	Crosicol	< INR 500,000	500000.0	34	2	1	1	500000.0	2	2	1	2	2	2	4	1	1	1	3	4	30.0	20	1	698	352	1.000	3	1	1	0	1	1	0	1	0	1	1	0	1	0	1	0	0	0	0	0
R_40ZEg7vX3Y7mMQl	184	R_40ZEg7vX3Y7mMQl	Yes, I would like to participate in the study ...	Female	31	Undergraduate Program	Not employed	4	6	2	2	2	1 (strongly disagree)	2	3	4	1 (strongly disagree)	30.0	30.0	10	0	20.0	10	0.0	1	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	True	500	10	10.000	59 out of 1000	50.0	20	They are equal	< INR 500,000	500000.0	31	2	2	1	500000.0	1	1	1	1	2	2	1	1	1	1	3	3	50.0	20	3	500	10	10.000	3	0	0	0	0	1	0	0	0	1	1	0	0	0	1	1	1	1	0	0
R_40cbsHWzTyKyFxv	53	R_40cbsHWzTyKyFxv	Yes, I would like to participate in the study ...	Male	38	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	5	5	4	6 (strongly agree)	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	45.0	35.0	0	5	15.0	0	0.0	2	1	2	Fingerprint	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	More than $102	Exactly the same as today with the money in th...	False	1000	100	25.000	9 out of 10	30.0	20	They are equal	< INR 500,000	300000.0	38	3	2	1	300000.0	2	1	2	2	2	2	2	2	2	2	3	4	30.0	20	3	1000	100	25.000	2	1	0	1	1	1	0	0	1	0	0	0	1	0	1	1	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
R_4xj4ficLsaoMZm9	138	R_4xj4ficLsaoMZm9	Yes, I would like to participate in the study ...	Male	20	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	1	3	2	4	5	5	4	3	6 (strongly agree)	90.0	0.0	0	0	0.0	10	0.0	2	2	1	HIV test	Irrespective of the quality of the study, futu...	The growth rate will be 0.4% on average each year	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Exactly the same as today with the money in th...	True	500	5	10.000	9 out of 10	26.0	20	Hertinol	INR 30,00,001 – INR 50,00,000	800000.0	20	2	2	5	800000.0	1	2	2	2	2	4	1	2	2	2	1	1	26.0	20	2	500	5	10.000	2	0	1	1	1	1	0	0	1	0	0	1	0	0	1	0	1	0	0	0
R_4ygaLyBv8lHSSf7	75	R_4ygaLyBv8lHSSf7	Yes, I would like to participate in the study ...	Male	34	Undergraduate Program	Entrepreneur/Business Owner in a sector other ...	4	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	6 (strongly agree)	1 (strongly disagree)	6 (strongly agree)	6 (strongly agree)	40.0	10.0	10	10	10.0	10	10.0	1	1	2	Fingerprint	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	Refuse to answer	500	999	100.000	9 out of 59	80.0	20	Hertinol	< INR 500,000	500000.0	34	2	2	1	500000.0	2	1	2	2	2	2	1	1	1	1	3	3	80.0	20	2	500	999	100.000	1	1	0	1	1	1	0	0	0	1	1	0	0	0	1	0	1	0	0	1
R_4yiENHF5RuMjf3P	318	R_4yiENHF5RuMjf3P	Yes, I would like to participate in the study ...	Male	42	Post-Graduate Program	Salaried/Employee/Consultant in a sector other...	5	5	6 (strongly agree)	5	6 (strongly agree)	5	6 (strongly agree)	5	6 (strongly agree)	6 (strongly agree)	20.0	50.0	0	19	10.0	0	1.0	1	2	2	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	False	500	10	1.000	9 out of 10	25.0	20	They are equal	< INR 500,000	600000.0	42	3	1	1	600000.0	2	2	1	2	2	2	3	1	1	1	3	1	25.0	20	3	500	10	1.000	2	1	1	0	1	1	0	0	0	1	1	0	0	1	1	1	1	1	0	0
R_4zPiSk23ayGvFD4	364	R_4zPiSk23ayGvFD4	Yes, I would like to participate in the study ...	Male	70	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	6	4	4	4	4	5	5	5	4	15.0	20.0	10	0	25.0	30	0.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	1.000	59 out of 1000	25.0	20	Crosicol	INR 1500,001 – INR 30,00,000	7000000.0	70	4	2	3	7000000.0	2	2	1	2	2	2	4	2	2	2	1	4	25.0	20	1	500	10	1.000	3	1	1	0	1	1	0	1	1	0	0	1	1	1	1	0	1	1	0	0
R_8k6D0jzzHCC5X3Z	369	R_8k6D0jzzHCC5X3Z	Yes, I would like to participate in the study ...	Male	25	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	7 (willing to take risk)	8	6 (strongly agree)	5	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	5.0	5.0	5	5	30.0	20	30.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	500	50.000	59 out of 1000	25.0	10	Hertinol	> INR 75,00,000	8000000.0	25	2	2	6	8000000.0	2	2	1	2	2	2	4	2	2	2	1	1	25.0	10	2	500	500	50.000	3	1	1	0	1	1	0	1	1	0	0	1	0	1	0	0	1	0	0	0

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
0	R_1YkMM2lMB9aEuVL	41	3	2	1	25000.0	2	1	1	2	0	66.666667	25.0	33.333333	66.666667	0.0	6	46.153846
1	R_401xXJ9E3ZgU9gu	33	2	2	2	16000000.0	1	1	3	1	1	33.333333	25.0	100.000000	33.333333	100.0	7	53.846154
2	R_40VyTmJ6i96wUP4	34	2	1	1	500000.0	2	0	1	2	0	66.666667	0.0	33.333333	66.666667	0.0	5	38.461538
3	R_40ZEg7vX3Y7mMQl	31	2	2	1	500000.0	0	2	2	2	0	0.000000	50.0	66.666667	66.666667	0.0	6	46.153846
4	R_40cbsHWzTyKyFxv	38	3	2	1	300000.0	2	0	2	1	0	66.666667	0.0	66.666667	33.333333	0.0	5	38.461538
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
265	R_4xj4ficLsaoMZm9	20	2	2	5	800000.0	2	1	1	1	1	66.666667	25.0	33.333333	33.333333	100.0	6	46.153846
266	R_4ygaLyBv8lHSSf7	34	2	2	1	500000.0	2	2	1	2	0	66.666667	50.0	33.333333	66.666667	0.0	7	53.846154
267	R_4yiENHF5RuMjf3P	42	3	1	1	600000.0	2	2	3	2	0	66.666667	50.0	100.000000	66.666667	0.0	9	69.230769
268	R_4zPiSk23ayGvFD4	70	4	2	3	7000000.0	2	2	2	1	1	66.666667	50.0	66.666667	33.333333	100.0	8	61.538462
269	R_8k6D0jzzHCC5X3Z	25	2	2	6	8000000.0	2	1	1	1	1	66.666667	25.0	33.333333	33.333333	100.0	6	46.153846

DATA CLEANING AND WRANGLING¶

Typeform Data¶

Demographics¶

Scoring¶

BeSample Data¶

Scoring¶

GRAPHS¶

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
0	R_4EaOO3SIpDoBOQ7	33.0	2	2	1	2500000.0	0	3	3	2	1	0.000000	75.0	100.000000	66.666667	100.0	9	69.230769
1	tmkt2yy447ml5n5xzvtmkt2yghkchd0i	30.5	2	1	1	1500000.0	1	2	1	3	0	33.333333	50.0	33.333333	100.000000	0.0	7	50.000000
2	abucqbywl1kpsyqxf6pabucqbyuj9d1t	30.5	2	1	1	1000000.0	0	2	2	2	0	0.000000	50.0	66.666667	66.666667	0.0	6	42.857143
3	lisjggm1u63hbw5au9dlis5v74im02su	40.5	3	1	1	470000.0	0	1	1	2	0	0.000000	25.0	33.333333	66.666667	0.0	4	28.571429
4	R_4KoJRZGedem1yYC	28.0	2	1	1	400000.0	2	2	1	2	1	66.666667	50.0	33.333333	66.666667	100.0	8	61.538462
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	xnbk00s7gpypscogm7i8w6xnbk0ndi7c	40.5	3	1	6	50000000.0	0	2	3	3	1	0.000000	50.0	100.000000	100.000000	100.0	9	64.285714
902	R_4ogXvXRdf6xjG2Z	26.0	2	2	6	650000.0	1	2	0	1	0	33.333333	50.0	0.000000	33.333333	0.0	4	30.769231
903	x4cenwxf77ta9w8t33vx4cenwxmm8ygb	70.5	4	1	6	50000000.0	1	3	1	3	1	33.333333	75.0	33.333333	100.000000	100.0	9	64.285714
904	i4ipejjfq0rnwbbi4ipe025xluvm5s8d	40.5	3	1	6	850000.0	1	0	0	1	1	33.333333	0.0	0.000000	33.333333	100.0	3	21.428571
905	R_8k6D0jzzHCC5X3Z	25.0	2	2	6	8000000.0	2	1	1	1	1	66.666667	25.0	33.333333	33.333333	100.0	6	46.153846

	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_%
income
1	38.400000	30.600000	44.000000	54.133333	24.800000	41.494505
2	25.965858	33.153639	45.193172	54.896676	26.684636	39.268683
3	25.450450	30.236486	38.963964	57.207207	27.702703	37.251262
4	21.739130	28.804348	32.608696	56.521739	23.913043	33.695652
5	25.531915	28.723404	42.553191	47.517730	36.170213	36.310498
6	25.757576	30.681818	34.090909	49.242424	25.000000	34.640360

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%	Quartile Number
0	R_4MJAzsNYYA69Y8p	27.0	2	3	1	0.0	2	1	1	2	1	66.666667	25.0	33.333333	66.666667	100.0	7	53.846154	1
1	y139mcxc619h0ooosy139muk14mpfpvm	30.5	2	3	1	0.0	3	1	0	2	0	100.000000	25.0	0.000000	66.666667	0.0	6	42.857143	1
2	R_41bfnamM0zpH94i	30.0	2	1	2	0.0	2	2	1	1	1	66.666667	50.0	33.333333	33.333333	100.0	7	53.846154	1
3	2rkur9ckjvsyz36zmayz1zw2rkur9ckn	40.5	3	1	3	0.0	1	0	0	2	0	33.333333	0.0	0.000000	66.666667	0.0	3	21.428571	1
4	e7ty2tbwa1hcmte7ty683aw64pla7689	30.5	2	2	1	0.0	0	1	1	1	0	0.000000	25.0	33.333333	33.333333	0.0	3	21.428571	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	lmoqk1qcf44jqqudwkplmoqk18cao0fs	40.5	3	1	2	400000000.0	1	1	1	1	0	33.333333	25.0	33.333333	33.333333	0.0	4	28.571429	4
902	4udfypw7lf8mmgjmxr4d4udfym4sx1sf	40.5	3	1	3	500000000.0	0	0	0	2	1	0.000000	0.0	0.000000	66.666667	100.0	3	21.428571	4
903	35zux4sc18rplyz3dc85z35zux4sdfm4	21.5	2	2	4	500000000.0	1	1	0	1	0	33.333333	25.0	0.000000	33.333333	0.0	3	21.428571	4
904	wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc	50.5	3	1	3	500000000.0	1	2	3	3	0	33.333333	50.0	100.000000	100.000000	0.0	9	64.285714	4
905	zcj7ldokhyo6217f9sriwizcj7ldodwt	40.5	3	2	3	600000000.0	1	3	1	1	0	33.333333	75.0	33.333333	33.333333	0.0	6	42.857143	4

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%	Quartile Number
240	9vob2u1c6rq0f6v9vob2ds01nmccl00r	50.5	3	1	2	350000.0	0	1	2	2	0	0.000000	25.0	66.666667	66.666667	0.0	5	35.714286	2
241	istj39htyy0gb6icisdl6lk7zoabotd5	30.5	2	2	1	350000.0	2	0	0	2	1	66.666667	0.0	0.000000	66.666667	100.0	5	35.714286	2
242	p7ung37hpf0ejr08izmp7u2wron7wo0a	50.5	3	2	2	350000.0	1	1	2	3	0	33.333333	25.0	66.666667	100.000000	0.0	7	50.000000	2
243	qqsa84yklzt7mi9qqsa9kp15tsjflbpn	30.5	2	2	2	350000.0	0	1	1	2	1	0.000000	25.0	33.333333	66.666667	100.0	5	35.714286	2
244	sm67z605u7x7he9zjss0qdbjsm67z6wc	21.5	2	1	3	350000.0	1	3	1	0	0	33.333333	75.0	33.333333	0.000000	0.0	5	35.714286	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
464	mohs0uis6exfikhnm2mohu6jsby2s4g3	30.5	2	1	2	1000000.0	0	2	1	3	1	0.000000	50.0	33.333333	100.000000	100.0	7	50.000000	2
465	8bkbabwbsdxut8bkba8zmczwciojohj6	30.5	2	1	3	1000000.0	0	1	1	3	0	0.000000	25.0	33.333333	100.000000	0.0	5	35.714286	2
466	R_4vy8lWFpUdr72G5	28.0	2	1	2	1000000.0	2	2	1	2	0	66.666667	50.0	33.333333	66.666667	0.0	7	53.846154	2
467	R_42xI7hoQbmBNStm	24.0	2	1	1	1000000.0	2	2	1	1	0	66.666667	50.0	33.333333	33.333333	0.0	6	46.153846	2
468	8q6vzofv5tq9lyy7ad8d5e58q6vzofve	21.5	2	2	2	1000000.0	0	1	2	3	0	0.000000	25.0	66.666667	100.000000	0.0	6	42.857143	2

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%	Quartile Number
469	10utcgrue12b2xedn0r10utc1is68haw	50.5	3	1	2	1005000.0	0	2	0	2	0	0.000000	50.0	0.000000	66.666667	0.0	4	28.571429	3
470	R_4aVnI292kkUXdvf	33.0	2	2	2	1050000.0	2	0	2	1	0	66.666667	0.0	66.666667	33.333333	0.0	5	38.461538	3
471	4r28kn5y6hze7b830b1z14r28ny2y8ms	21.5	2	1	2	1050000.0	1	1	1	1	0	33.333333	25.0	33.333333	33.333333	0.0	4	28.571429	3
472	7rjksqqby6at13qv93d7rjksvn67w2du	30.5	2	1	2	1100000.0	0	0	3	3	1	0.000000	0.0	100.000000	100.000000	100.0	7	50.000000	3
473	lou9povqizdfljz9255xlou9poadabjr	30.5	2	2	2	1150000.0	0	2	2	2	0	0.000000	50.0	66.666667	66.666667	0.0	6	42.857143	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
693	g7ih764s6co9a5zsr4g7ih7631ybkd3v	30.5	2	1	2	5000000.0	0	1	1	1	1	0.000000	25.0	33.333333	33.333333	100.0	4	28.571429	3
694	n5tap3d3v5102n5tay187lv3mwzs3sjy	40.5	3	1	2	5000000.0	0	0	1	1	0	0.000000	0.0	33.333333	33.333333	0.0	2	14.285714	3
695	1g5v4ycgo16lqs9d1g5v5b6b8lnjv6q7	40.5	3	1	4	5000000.0	0	3	2	3	1	0.000000	75.0	66.666667	100.000000	100.0	9	64.285714	3
696	48m1y9k42nj4pk48m43udwbzp5y189z0	30.5	2	2	2	5000000.0	2	2	1	3	0	66.666667	50.0	33.333333	100.000000	0.0	8	57.142857	3
697	93yix8e0e384z7s692mf93yix8edfg15	30.5	2	2	1	5000000.0	3	1	2	0	0	100.000000	25.0	66.666667	0.000000	0.0	6	42.857143	3

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%	Quartile Number
698	obftinjncjw2fkari22ghobfti6alubg	30.5	2	1	3	5200000.0	0	1	3	3	0	0.000000	25.0	100.000000	100.000000	0.0	7	50.000000	4
699	rh3ida1re0oohl9crh3ida1s6gcynlil	30.5	2	1	4	5245899.0	3	0	1	1	0	100.000000	0.0	33.333333	33.333333	0.0	5	35.714286	4
700	gdsir9721na619gpfsu9jqk7bi4rhpfs	30.5	2	2	3	5500000.0	1	3	2	2	1	33.333333	75.0	66.666667	66.666667	100.0	9	64.285714	4
701	R_4v7duCkCj5syx0Z	26.0	2	2	2	5500000.0	2	0	2	2	0	66.666667	0.0	66.666667	66.666667	0.0	6	46.153846	4
702	fa4fw88qzaxzjxw31rzchtfa4fw8gn8h	30.5	2	1	3	5500000.0	2	1	0	2	0	66.666667	25.0	0.000000	66.666667	0.0	5	35.714286	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	lmoqk1qcf44jqqudwkplmoqk18cao0fs	40.5	3	1	2	400000000.0	1	1	1	1	0	33.333333	25.0	33.333333	33.333333	0.0	4	28.571429	4
902	4udfypw7lf8mmgjmxr4d4udfym4sx1sf	40.5	3	1	3	500000000.0	0	0	0	2	1	0.000000	0.0	0.000000	66.666667	100.0	3	21.428571	4
903	35zux4sc18rplyz3dc85z35zux4sdfm4	21.5	2	2	4	500000000.0	1	1	0	1	0	33.333333	25.0	0.000000	33.333333	0.0	3	21.428571	4
904	wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc	50.5	3	1	3	500000000.0	1	2	3	3	0	33.333333	50.0	100.000000	100.000000	0.0	9	64.285714	4
905	zcj7ldokhyo6217f9sriwizcj7ldodwt	40.5	3	2	3	600000000.0	1	3	1	1	0	33.333333	75.0	33.333333	33.333333	0.0	6	42.857143	4

	ResponseId	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
0	q3xfqbasiduvbbq3xftruekpjog3g58m	40.5	3	1	2	8000000.0	0	1	3	3	0	0.000000	25.0	100.000000	100.000000	0.0	7	50.000000
1	R_4PjnTbc2vIjogot	31.0	2	1	1	200000.0	2	1	0	1	0	66.666667	25.0	0.000000	33.333333	0.0	4	30.769231
2	R_4Ph1UNtYnaoPSX7	25.0	2	1	3	400000.0	0	2	0	1	0	0.000000	50.0	0.000000	33.333333	0.0	3	23.076923
3	n5tap3d3v5102n5tay187lv3mwzs3sjy	40.5	3	1	2	5000000.0	0	0	1	1	0	0.000000	0.0	33.333333	33.333333	0.0	2	14.285714
4	n6oeoyadt6ogysn6oeoy85zyh3urcai8	50.5	3	1	3	7500000.0	0	2	3	3	1	0.000000	50.0	100.000000	100.000000	100.0	9	64.285714
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	tljgvfjwoh3aj1i4hdk8wtphtljgjahd	70.5	4	3	1	2500000.0	1	1	3	3	0	33.333333	25.0	100.000000	100.000000	0.0	8	57.142857
902	y139mcxc619h0ooosy139muk14mpfpvm	30.5	2	3	1	0.0	3	1	0	2	0	100.000000	25.0	0.000000	66.666667	0.0	6	42.857143
903	R_4IY67BO1uGe00kp	39.0	3	3	2	35000.0	2	1	0	1	0	66.666667	25.0	0.000000	33.333333	0.0	4	30.769231
904	ylqbir1q5isg86n72mvz9jylqbirf512	30.5	2	3	2	50000.0	1	0	0	1	0	33.333333	0.0	0.000000	33.333333	0.0	2	14.285714
905	4h20tw0737azsz4k54h20tfe79kj50cq	21.5	2	3	1	100000.0	0	1	0	0	1	0.000000	25.0	0.000000	0.000000	100.0	2	14.285714

	Certainty_%	RiskComprehension_%	Numeracy_%	GraphLiteracy_%	Bayesianreasoning_%
isced
1	26.485788	30.862403	54.844961	41.731266	24.418605
2	31.835206	33.005618	54.307116	44.850187	30.056180
3	39.215686	22.794118	50.980392	30.392157	23.529412

	wealth
count	5.160000e+02
mean	1.070025e+07
std	4.079317e+07
min	0.000000e+00
25%	4.000000e+05
50%	1.500000e+06
75%	7.000000e+06
max	5.000000e+08

	wealth
count	3.560000e+02
mean	9.234711e+06
std	4.620976e+07
min	0.000000e+00
25%	3.000000e+05
50%	8.000000e+05
75%	4.625000e+06
max	6.000000e+08

	ResponseId
isced
1	516
2	356
3	34

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	34.000000	34.000000	34.0	34.000000	3.400000e+01	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000
mean	34.176471	2.441176	3.0	1.705882	2.461516e+06	1.176471	0.911765	0.911765	1.529412	0.235294	39.215686	22.794118	30.392157	50.980392	23.529412	4.764706	35.035553
std	13.167270	0.746352	0.0	1.268008	5.835454e+06	0.968303	0.668225	1.025955	0.991946	0.430562	32.276779	16.705614	34.198516	33.064873	43.056155	2.175049	15.969943
min	16.000000	0.000000	3.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	22.500000	2.000000	3.0	1.000000	3.125000e+04	0.000000	0.250000	0.000000	1.000000	0.000000	0.000000	6.250000	0.000000	33.333333	0.000000	3.000000	21.428571
50%	30.500000	2.000000	3.0	1.000000	2.000000e+05	1.000000	1.000000	1.000000	1.000000	0.000000	33.333333	25.000000	33.333333	33.333333	0.000000	5.000000	35.714286
75%	40.500000	3.000000	3.0	2.000000	1.875000e+06	2.000000	1.000000	2.000000	2.000000	0.000000	66.666667	25.000000	66.666667	66.666667	0.000000	6.000000	46.153846
max	70.500000	4.000000	3.0	6.000000	3.000000e+07	3.000000	2.000000	3.000000	3.000000	1.000000	100.000000	50.000000	100.000000	100.000000	100.000000	9.000000	64.285714

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	586.000000	586.0	586.000000	586.000000	5.860000e+02	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000
mean	28.216724	2.0	1.503413	2.225256	7.457495e+06	0.877133	1.259386	1.283276	1.616041	0.259386	29.237770	31.484642	42.775882	53.868032	25.938567	5.295222	38.970671
std	3.873640	0.0	0.558529	1.258317	2.899640e+07	0.887861	0.926876	0.966955	0.891181	0.438672	29.595359	23.171909	32.231832	29.706037	43.867200	2.129133	16.023638
min	18.000000	2.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.000000	2.0	1.000000	1.000000	3.000000e+05	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	25.000000	33.333333	33.333333	0.000000	4.000000	28.571429
50%	30.500000	2.0	1.000000	2.000000	1.000000e+06	1.000000	1.000000	1.000000	2.000000	0.000000	33.333333	25.000000	33.333333	66.666667	0.000000	5.000000	38.461538
75%	30.500000	2.0	2.000000	3.000000	4.000000e+06	2.000000	2.000000	2.000000	2.000000	1.000000	66.666667	50.000000	66.666667	66.666667	100.000000	7.000000	50.000000
max	35.000000	2.0	3.000000	6.000000	5.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	84.615385

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	285.000000	285.0	285.000000	285.000000	2.850000e+02	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000
mean	42.577193	3.0	1.385965	2.578947	1.463604e+07	0.856140	1.238596	1.235088	1.666667	0.280702	28.538012	30.964912	41.169591	55.555556	28.070175	5.277193	38.401774
std	4.501683	0.0	0.573916	1.423487	6.251005e+07	0.882342	0.887661	0.984473	0.914156	0.450132	29.411398	22.191520	32.815766	30.471859	45.013245	2.260423	16.624520
min	36.000000	3.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	40.500000	3.0	1.000000	2.000000	3.000000e+05	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	25.000000	0.000000	33.333333	0.000000	3.000000	23.076923
50%	40.500000	3.0	1.000000	2.000000	2.000000e+06	1.000000	1.000000	1.000000	2.000000	0.000000	33.333333	25.000000	33.333333	66.666667	0.000000	5.000000	35.714286
75%	44.000000	3.0	2.000000	3.000000	7.500000e+06	2.000000	2.000000	2.000000	2.000000	1.000000	66.666667	50.000000	66.666667	66.666667	100.000000	7.000000	50.000000
max	54.000000	3.0	3.000000	6.000000	6.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	78.571429

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	516.000000	516.000000	516.0	516.000000	5.160000e+02	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000
mean	35.114341	2.437984	1.0	2.593023	1.070025e+07	0.794574	1.234496	1.251938	1.645349	0.244186	26.485788	30.862403	41.731266	54.844961	24.418605	5.170543	37.673567
std	9.188189	0.566055	0.0	1.358565	4.079317e+07	0.854713	0.879790	0.960631	0.947248	0.430020	28.490420	21.994758	32.021031	31.574943	43.002045	2.142946	15.823234
min	20.000000	2.000000	1.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.0	2.000000	4.000000e+05	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	25.000000	33.333333	33.333333	0.000000	3.000000	23.076923
50%	30.500000	2.000000	1.0	2.000000	1.500000e+06	1.000000	1.000000	1.000000	2.000000	0.000000	33.333333	25.000000	33.333333	66.666667	0.000000	5.000000	35.714286
75%	40.500000	3.000000	1.0	3.000000	7.000000e+06	1.000000	2.000000	2.000000	2.000000	0.000000	33.333333	50.000000	66.666667	66.666667	0.000000	7.000000	50.000000
max	70.500000	4.000000	1.0	6.000000	5.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	78.571429

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	356.000000	356.000000	356.0	356.000000	3.560000e+02	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000
mean	32.424157	2.308989	2.0	2.030899	9.234711e+06	0.955056	1.320225	1.345506	1.629213	0.300562	31.835206	33.005618	44.850187	54.307116	30.056180	5.550562	41.026670
std	9.745053	0.536056	0.0	1.211053	4.620976e+07	0.902705	0.963407	0.976488	0.817168	0.459148	30.090157	24.085177	32.549604	27.238940	45.914767	2.175465	16.442525
min	18.000000	2.000000	2.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.000000	2.000000	2.0	1.000000	3.000000e+05	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	25.000000	33.333333	33.333333	0.000000	4.000000	28.571429
50%	30.500000	2.000000	2.0	2.000000	8.000000e+05	1.000000	1.000000	1.000000	2.000000	0.000000	33.333333	25.000000	33.333333	66.666667	0.000000	5.000000	38.461538
75%	38.250000	3.000000	2.0	2.000000	4.625000e+06	2.000000	2.000000	2.000000	2.000000	1.000000	66.666667	50.000000	66.666667	66.666667	100.000000	7.000000	53.846154
max	70.000000	4.000000	2.0	6.000000	6.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	84.615385

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	233.000000	233.000000	233.000000	233.000000	2.330000e+02	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000
mean	24.143777	1.991416	1.695279	2.025751	8.812035e+06	1.145923	1.364807	1.364807	1.557940	0.317597	38.197425	34.120172	45.493562	51.931330	31.759657	5.751073	43.184927
std	3.100287	0.131024	0.569955	1.220948	4.168456e+07	0.912175	0.933088	0.964882	0.775341	0.466544	30.405830	23.327193	32.162737	25.844715	46.654385	2.059003	15.735196
min	16.000000	0.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	7.142857
25%	21.500000	2.000000	1.000000	1.000000	2.000000e+05	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	25.000000	33.333333	33.333333	0.000000	4.000000	30.769231
50%	23.000000	2.000000	2.000000	2.000000	5.000000e+05	1.000000	1.000000	1.000000	1.000000	0.000000	33.333333	25.000000	33.333333	33.333333	0.000000	6.000000	42.857143
75%	27.000000	2.000000	2.000000	2.000000	2.500000e+06	2.000000	2.000000	2.000000	2.000000	1.000000	66.666667	50.000000	66.666667	66.666667	100.000000	7.000000	53.846154
max	30.000000	2.000000	3.000000	6.000000	5.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	84.615385

	age	age_rec	isced	income	wealth	Certainty_3	RiskComprehension_3	GraphLiteracy_3	Numeracy_3	Bayesianreasoning_1	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_13	TotalScore_%
count	673.000000	673.000000	673.000000	673.000000	6.730000e+02	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000
mean	37.442051	2.524517	1.389302	2.447251	1.016252e+07	0.777117	1.218425	1.245171	1.661218	0.248143	25.903913	30.460624	41.505696	55.373947	24.814264	5.150074	37.405908
std	8.762724	0.592419	0.548270	1.352014	4.252269e+07	0.852664	0.898818	0.973264	0.937605	0.432256	28.422118	22.470457	32.442124	31.253513	43.225629	2.180761	16.045282
min	30.500000	2.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.000000	2.000000	3.750000e+05	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	25.000000	0.000000	33.333333	0.000000	3.000000	23.076923
50%	34.000000	2.000000	1.000000	2.000000	1.500000e+06	1.000000	1.000000	1.000000	2.000000	0.000000	33.333333	25.000000	33.333333	66.666667	0.000000	5.000000	35.714286
75%	40.500000	3.000000	2.000000	3.000000	6.000000e+06	1.000000	2.000000	2.000000	2.000000	0.000000	33.333333	50.000000	66.666667	66.666667	0.000000	7.000000	50.000000
max	70.500000	4.000000	3.000000	6.000000	6.000000e+08	3.000000	3.000000	3.000000	3.000000	1.000000	100.000000	75.000000	100.000000	100.000000	100.000000	11.000000	78.571429